• Structural Engineering and Mechanics
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• 제87권6호
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• pp.585-599
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• 2023

Geopolymer concrete (GC) can be competently utilized as a practical replacement for cement to prevent a high carbon footprint and to give a direction toward sustainable concrete construction. Moreover, previous studies mostly focused on the axial response of glass fiber reinforced polymer (glass-FRP) concrete compressive elements without determining the effectiveness of repairing them after their partial damage. The goal of this study is to assess the structural effectiveness of partially damaged GC columns that have been restored using carbon fiber reinforced polymer (carbon-FRP). Bars made of glass-FRP and helix made of glass-FRP are used to reinforce these columns. For comparative study, six of the twelve circular specimens-each measuring 300 mm×1200 mm-are reinforced with steel bars, while the other four are axially strengthened using glass-FRP bars (referred to as GSG columns). The broken columns are repaired and strengthened using carbon-FRP sheets after the specimens have been subjected to concentric and eccentric compression until a 30% loss in axial strength is attained in the post-peak phase. The study investigates the effects of various variables on important response metrics like axial strength, axial deflection, load-deflection response, stiffness index, strength index, ductility index, and damage response. These variables include concentric and eccentric compression, helix pitch, steel bars, carbon-FRP wrapping, and glass-FRP bars. Both before and after the quick repair process, these metrics are evaluated. The results of the investigation show that the axial strengths of the reconstructed SSG and GSG columns are, respectively, 15.3% and 20.9% higher than those of their original counterparts. In addition, compared to their SSG counterparts, the repaired GSG samples exhibit an improvement in average ductility indices of 2.92% and a drop in average stiffness indices of 3.2%.

• Steel and Composite Structures
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• 제49권2호
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• pp.161-180
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• 2023

In the previous research, the axial compressive capacity models for the glass fiber-reinforced polymer (GFRP)-reinforced circular concrete compression elements restrained with GFRP helix were put forward based on small and noisy datasets by considering a limited number of parameters portraying less accuracy. Consequently, it is important to recommend an accurate model based on a refined and large testing dataset that considers various parameters of such components. The core objective and novelty of the current research is to suggest a deep learning model for the axial compressive capacity of GFRP-reinforced circular concrete columns restrained with a GFRP helix utilizing various parameters of a large experimental dataset to give the maximum precision of the estimates. To achieve this aim, a test dataset of 61 GFRP-reinforced circular concrete columns restrained with a GFRP helix has been created from prior studies. An assessment of 15 diverse theoretical models is carried out utilizing different statistical coefficients over the created dataset. A novel model utilizing the group method of data handling (GMDH) has been put forward. The recommended model depicted good effectiveness over the created dataset by assuming the axial involvement of GFRP main bars and the confining effectiveness of transverse GFRP helix and depicted the maximum precision with MAE = 195.67, RMSE = 255.41, and R2 = 0.94 as associated with the previously recommended equations. The GMDH model also depicted good effectiveness for the normal distribution of estimates with only a 2.5% discrepancy from unity. The recommended model can accurately calculate the axial compressive capacity of FRP-reinforced concrete compression elements that can be considered for further analysis and design of such components in the field of structural engineering.